Overview

Dataset statistics

Number of variables9
Number of observations165166
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)< 0.1%
Total size in memory12.6 MiB
Average record size in memory80.0 B

Variable types

Numeric9

Alerts

Dataset has 12 (< 0.1%) duplicate rowsDuplicates
voltage is highly overall correlated with current and 2 other fieldsHigh correlation
current is highly overall correlated with voltage and 2 other fieldsHigh correlation
workstation_cpu is highly overall correlated with voltage and 3 other fieldsHigh correlation
workstation_gpu is highly overall correlated with workstation_cpuHigh correlation
workstation_ram is highly overall correlated with voltage and 2 other fieldsHigh correlation
esp32_temperature has 27910 (16.9%) zerosZeros
workstation_cpu has 92498 (56.0%) zerosZeros
workstation_gpu has 142018 (86.0%) zerosZeros
workstation_ram has 92498 (56.0%) zerosZeros

Reproduction

Analysis started2023-07-28 04:02:27.571223
Analysis finished2023-07-28 04:02:32.874773
Duration5.3 seconds
Software versionydata-profiling vv4.3.2
Download configurationconfig.json

Variables

voltage
Real number (ℝ)

HIGH CORRELATION 

Distinct5855
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.04762
Minimum118.2
Maximum120.52632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-07-28T06:02:32.906338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum118.2
5-th percentile119.8
Q1119.99665
median120.05664
Q3120.15088
95-th percentile120.48571
Maximum120.52632
Range2.3263158
Interquartile range (IQR)0.15423153

Descriptive statistics

Standard deviation0.24040811
Coefficient of variation (CV)0.0020026062
Kurtosis7.9926749
Mean120.04762
Median Absolute Deviation (MAD)0.0776898
Skewness-2.0783572
Sum19827785
Variance0.057796059
MonotonicityNot monotonic
2023-07-28T06:02:32.957343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.2 4788
 
2.9%
120.5 3215
 
1.9%
120.1 1739
 
1.1%
120.1982143 865
 
0.5%
120.1964286 741
 
0.4%
120.1982456 719
 
0.4%
120 691
 
0.4%
120.1946429 691
 
0.4%
120.05 683
 
0.4%
120.1928571 643
 
0.4%
Other values (5845) 150391
91.1%
ValueCountFrequency (%)
118.2 3
< 0.1%
118.2545455 1
 
< 0.1%
118.2636364 1
 
< 0.1%
118.2681818 1
 
< 0.1%
118.275 3
< 0.1%
118.2840909 3
< 0.1%
118.2860465 1
 
< 0.1%
118.2886364 1
 
< 0.1%
118.2909091 1
 
< 0.1%
118.2931818 1
 
< 0.1%
ValueCountFrequency (%)
120.5263158 1
 
< 0.1%
120.525 1
 
< 0.1%
120.5157895 1
 
< 0.1%
120.5142857 1
 
< 0.1%
120.5140351 1
 
< 0.1%
120.5125 3
< 0.1%
120.5122807 1
 
< 0.1%
120.5107143 1
 
< 0.1%
120.5105263 2
 
< 0.1%
120.5089286 7
< 0.1%

current
Real number (ℝ)

HIGH CORRELATION 

Distinct20279
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79875057
Minimum0.02
Maximum1.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-07-28T06:02:33.002891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.13
Q10.62684211
median0.92027523
Q30.94375
95-th percentile1.0505937
Maximum1.59
Range1.57
Interquartile range (IQR)0.31690789

Descriptive statistics

Standard deviation0.24079029
Coefficient of variation (CV)0.30145868
Kurtosis1.5638647
Mean0.79875057
Median Absolute Deviation (MAD)0.11814582
Skewness-1.2679018
Sum131926.44
Variance0.057979965
MonotonicityNot monotonic
2023-07-28T06:02:33.048456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 11336
 
6.9%
0.62125 544
 
0.3%
0.6214285714 486
 
0.3%
0.6210714286 477
 
0.3%
0.6216071429 415
 
0.3%
0.6214035088 410
 
0.2%
0.6212280702 396
 
0.2%
0.6210526316 396
 
0.2%
0.6208928571 346
 
0.2%
0.6215789474 340
 
0.2%
Other values (20269) 150020
90.8%
ValueCountFrequency (%)
0.02 58
 
< 0.1%
0.02 48
 
< 0.1%
0.1269642857 1
 
< 0.1%
0.1275 1
 
< 0.1%
0.1280357143 1
 
< 0.1%
0.1285714286 1
 
< 0.1%
0.1296428571 1
 
< 0.1%
0.13 11336
6.9%
0.1998214286 1
 
< 0.1%
0.2601785714 1
 
< 0.1%
ValueCountFrequency (%)
1.59 1
< 0.1%
1.439464286 1
< 0.1%
1.421754386 1
< 0.1%
1.421403509 1
< 0.1%
1.391785714 1
< 0.1%
1.380175439 1
< 0.1%
1.333928571 1
< 0.1%
1.325964912 1
< 0.1%
1.321428571 1
< 0.1%
1.312321429 1
< 0.1%

frequency
Real number (ℝ)

Distinct3313
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.965275
Minimum59.8
Maximum60.046429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-07-28T06:02:33.096627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum59.8
5-th percentile59.926667
Q159.950909
median59.967647
Q359.982143
95-th percentile59.996491
Maximum60.046429
Range0.24642857
Interquartile range (IQR)0.031233766

Descriptive statistics

Standard deviation0.021984268
Coefficient of variation (CV)0.00036661665
Kurtosis0.69872949
Mean59.965275
Median Absolute Deviation (MAD)0.015371809
Skewness-0.67131828
Sum9904224.6
Variance0.00048330805
MonotonicityNot monotonic
2023-07-28T06:02:33.145487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 3857
 
2.3%
59.975 2369
 
1.4%
59.97142857 2365
 
1.4%
59.97678571 2348
 
1.4%
59.97321429 2309
 
1.4%
59.96785714 2287
 
1.4%
59.96964286 2255
 
1.4%
59.9625 2253
 
1.4%
59.97857143 2251
 
1.4%
59.98035714 2212
 
1.3%
Other values (3303) 140660
85.2%
ValueCountFrequency (%)
59.8 1
< 0.1%
59.8 2
< 0.1%
59.8 1
< 0.1%
59.8010989 1
< 0.1%
59.80315789 1
< 0.1%
59.80357143 1
< 0.1%
59.805 1
< 0.1%
59.80535714 1
< 0.1%
59.80816327 1
< 0.1%
59.81071429 1
< 0.1%
ValueCountFrequency (%)
60.04642857 2
< 0.1%
60.04464286 1
< 0.1%
60.04347826 1
< 0.1%
60.04285714 1
< 0.1%
60.04205607 1
< 0.1%
60.0322314 1
< 0.1%
60.02654867 1
< 0.1%
60.0254902 1
< 0.1%
60.02105263 1
< 0.1%
60.01964286 1
< 0.1%

energy
Real number (ℝ)

Distinct57998
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.37721
Minimum0
Maximum432.50481
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-07-28T06:02:33.192953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.784
Q169.61
median136.68
Q3188.35
95-th percentile265.28268
Maximum432.50481
Range432.50481
Interquartile range (IQR)118.74

Descriptive statistics

Standard deviation80.15435
Coefficient of variation (CV)0.59208156
Kurtosis0.31323276
Mean135.37721
Median Absolute Deviation (MAD)59.82
Skewness0.46603792
Sum22359712
Variance6424.7198
MonotonicityNot monotonic
2023-07-28T06:02:33.237290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136.93 136
 
0.1%
136.62 136
 
0.1%
137.21 136
 
0.1%
136.74 136
 
0.1%
136.82 136
 
0.1%
136.85 136
 
0.1%
137.4 136
 
0.1%
137.1 136
 
0.1%
136.46 136
 
0.1%
137.43 136
 
0.1%
Other values (57988) 163806
99.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.02 6
< 0.1%
0.02819047619 1
 
< 0.1%
0.03 5
< 0.1%
0.03641509434 1
 
< 0.1%
0.04 4
< 0.1%
0.04 1
 
< 0.1%
0.04470588235 1
 
< 0.1%
0.05 6
< 0.1%
0.05653543307 1
 
< 0.1%
ValueCountFrequency (%)
432.5048056 1
< 0.1%
432.5033333 1
< 0.1%
432.50145 1
< 0.1%
432.4995667 1
< 0.1%
432.4978167 1
< 0.1%
432.4959833 1
< 0.1%
432.49415 1
< 0.1%
432.4924 1
< 0.1%
432.4905333 1
< 0.1%
432.4887333 1
< 0.1%

power_factor
Real number (ℝ)

Distinct7013
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85834136
Minimum0
Maximum0.97125
Zeros106
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-07-28T06:02:33.283452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.32035714
Q10.86160714
median0.89678571
Q30.91105263
95-th percentile0.96071429
Maximum0.97125
Range0.97125
Interquartile range (IQR)0.049445489

Descriptive statistics

Standard deviation0.15110143
Coefficient of variation (CV)0.17603886
Kurtosis8.7496339
Mean0.85834136
Median Absolute Deviation (MAD)0.033452381
Skewness-3.1657734
Sum141768.81
Variance0.022831643
MonotonicityNot monotonic
2023-07-28T06:02:33.330146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32 2836
 
1.7%
0.32 2274
 
1.4%
0.9 1297
 
0.8%
0.86 1211
 
0.7%
0.8992857143 1016
 
0.6%
0.8991071429 1012
 
0.6%
0.8996428571 1004
 
0.6%
0.8994642857 969
 
0.6%
0.8989285714 968
 
0.6%
0.8985714286 900
 
0.5%
Other values (7003) 151679
91.8%
ValueCountFrequency (%)
0 106
0.1%
0.18 1
 
< 0.1%
0.2992857143 1
 
< 0.1%
0.30125 1
 
< 0.1%
0.3046428571 1
 
< 0.1%
0.3147368421 2
 
< 0.1%
0.3150877193 1
 
< 0.1%
0.3151785714 2
 
< 0.1%
0.3152631579 2
 
< 0.1%
0.3153571429 1
 
< 0.1%
ValueCountFrequency (%)
0.97125 1
 
< 0.1%
0.9710526316 3
 
< 0.1%
0.9708928571 3
 
< 0.1%
0.970877193 5
 
< 0.1%
0.9707142857 3
 
< 0.1%
0.9707017544 6
 
< 0.1%
0.9705357143 15
< 0.1%
0.9705263158 11
< 0.1%
0.9703571429 21
< 0.1%
0.9703508772 13
< 0.1%

esp32_temperature
Real number (ℝ)

ZEROS 

Distinct14063
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.122302
Minimum0
Maximum53.3333
Zeros27910
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-07-28T06:02:33.375902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126.54
median32.838929
Q333.87
95-th percentile34.970526
Maximum53.3333
Range53.3333
Interquartile range (IQR)7.33

Descriptive statistics

Standard deviation13.259469
Coefficient of variation (CV)0.48887697
Kurtosis0.54051986
Mean27.122302
Median Absolute Deviation (MAD)1.5723214
Skewness-1.0841636
Sum4479682.1
Variance175.81351
MonotonicityNot monotonic
2023-07-28T06:02:33.422129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27910
 
16.9%
53.3333 659
 
0.4%
33.88 475
 
0.3%
33.91946429 451
 
0.3%
33.92928571 416
 
0.3%
33.93910714 415
 
0.3%
33.89964286 399
 
0.2%
33.90946429 388
 
0.2%
33.90964286 371
 
0.2%
33.87982143 350
 
0.2%
Other values (14053) 133332
80.7%
ValueCountFrequency (%)
0 27910
16.9%
22.18543478 1
 
< 0.1%
22.70982143 1
 
< 0.1%
22.71122807 1
 
< 0.1%
22.71982143 1
 
< 0.1%
22.72982143 1
 
< 0.1%
22.73070175 1
 
< 0.1%
22.73982143 3
 
< 0.1%
22.74017857 1
 
< 0.1%
22.74052632 1
 
< 0.1%
ValueCountFrequency (%)
53.3333 659
0.4%
53.3333 2
 
< 0.1%
53.2455807 1
 
< 0.1%
53.17589333 1
 
< 0.1%
53.02864194 1
 
< 0.1%
53.01315085 1
 
< 0.1%
53.00543115 1
 
< 0.1%
53.0037339 1
 
< 0.1%
52.99996667 1
 
< 0.1%
52.99070833 1
 
< 0.1%

workstation_cpu
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34029
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1543831
Minimum0
Maximum48.828571
Zeros92498
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-07-28T06:02:33.466607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.742489
95-th percentile9.0597784
Maximum48.828571
Range48.828571
Interquartile range (IQR)4.742489

Descriptive statistics

Standard deviation3.2681706
Coefficient of variation (CV)1.5169867
Kurtosis2.5360367
Mean2.1543831
Median Absolute Deviation (MAD)0
Skewness1.5655673
Sum355830.84
Variance10.680939
MonotonicityNot monotonic
2023-07-28T06:02:33.512604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92498
56.0%
0.3357142857 63
 
< 0.1%
0.3446428571 62
 
< 0.1%
0.6339285714 62
 
< 0.1%
0.3428571429 61
 
< 0.1%
0.6607142857 60
 
< 0.1%
0.625 59
 
< 0.1%
0.6232142857 59
 
< 0.1%
0.6625 58
 
< 0.1%
0.3589285714 57
 
< 0.1%
Other values (34019) 72127
43.7%
ValueCountFrequency (%)
0 92498
56.0%
0.1 1
 
< 0.1%
0.1571428571 1
 
< 0.1%
0.1714285714 1
 
< 0.1%
0.1833333333 1
 
< 0.1%
0.1857142857 2
 
< 0.1%
0.1866666667 1
 
< 0.1%
0.19375 1
 
< 0.1%
0.1941176471 1
 
< 0.1%
0.2 1
 
< 0.1%
ValueCountFrequency (%)
48.82857143 1
< 0.1%
33.66315789 1
< 0.1%
27.94321429 1
< 0.1%
26.42178571 1
< 0.1%
25.95892857 1
< 0.1%
25.5325 1
< 0.1%
25.2675 1
< 0.1%
25.25947368 1
< 0.1%
23.9777193 1
< 0.1%
23.34678571 1
< 0.1%

workstation_gpu
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1208
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.032610643
Minimum0
Maximum6.375
Zeros142018
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-07-28T06:02:33.558743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.24561404
Maximum6.375
Range6.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.17149738
Coefficient of variation (CV)5.258939
Kurtosis189.20629
Mean0.032610643
Median Absolute Deviation (MAD)0
Skewness11.23747
Sum5386.1694
Variance0.029411351
MonotonicityNot monotonic
2023-07-28T06:02:33.602502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 142018
86.0%
0.01785714286 4013
 
2.4%
0.01754385965 2189
 
1.3%
0.03571428571 1255
 
0.8%
0.0350877193 722
 
0.4%
0.0007142857143 511
 
0.3%
0.000701754386 424
 
0.3%
0.25 346
 
0.2%
0.3214285714 337
 
0.2%
0.008333333333 298
 
0.2%
Other values (1198) 13053
 
7.9%
ValueCountFrequency (%)
0 142018
86.0%
0.0001754385965 29
 
< 0.1%
0.0001785714286 39
 
< 0.1%
0.0003333333333 11
 
< 0.1%
0.000350877193 27
 
< 0.1%
0.0003571428571 27
 
< 0.1%
0.0004166666667 1
 
< 0.1%
0.0004347826087 2
 
< 0.1%
0.0004761904762 1
 
< 0.1%
0.0005263157895 23
 
< 0.1%
ValueCountFrequency (%)
6.375 1
< 0.1%
5.545454545 1
< 0.1%
5.523364486 1
< 0.1%
5.272727273 1
< 0.1%
5.224489796 1
< 0.1%
5.125 1
< 0.1%
4.658333333 1
< 0.1%
4.6 1
< 0.1%
4.596491228 1
< 0.1%
4.541666667 1
< 0.1%

workstation_ram
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41043
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.453643
Minimum0
Maximum54.916071
Zeros92498
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-07-28T06:02:33.647722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337.279464
95-th percentile44.1
Maximum54.916071
Range54.916071
Interquartile range (IQR)37.279464

Descriptive statistics

Standard deviation18.882479
Coefficient of variation (CV)1.1476169
Kurtosis-1.7662416
Mean16.453643
Median Absolute Deviation (MAD)0
Skewness0.33886863
Sum2717582.3
Variance356.54801
MonotonicityNot monotonic
2023-07-28T06:02:33.688000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92498
56.0%
29 240
 
0.1%
28.2 236
 
0.1%
27.4 209
 
0.1%
26 192
 
0.1%
28.3 191
 
0.1%
27.2 191
 
0.1%
26.3 187
 
0.1%
28.7 180
 
0.1%
27.9 170
 
0.1%
Other values (41033) 70872
42.9%
ValueCountFrequency (%)
0 92498
56.0%
8.366666667 1
 
< 0.1%
9.6225 1
 
< 0.1%
10.028 1
 
< 0.1%
10.036 1
 
< 0.1%
10.044 1
 
< 0.1%
10.48813084 1
 
< 0.1%
10.49337838 1
 
< 0.1%
10.75489796 1
 
< 0.1%
11.01625 1
 
< 0.1%
ValueCountFrequency (%)
54.91607143 1
 
< 0.1%
54.30178571 1
 
< 0.1%
54.15263158 1
 
< 0.1%
53.80178571 1
 
< 0.1%
53.59649123 1
 
< 0.1%
53.50892857 1
 
< 0.1%
53.34035088 1
 
< 0.1%
53.22105263 1
 
< 0.1%
53.20714286 1
 
< 0.1%
53.2 3
< 0.1%

Interactions

2023-07-28T06:02:32.089415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:28.741968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.144620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.564609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.976065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.451469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.860928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.265513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.677191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:32.134479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:28.785686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.189109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.609536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.017657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.495604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.905195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.312392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.721922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:32.182745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:28.834115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.238711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.658475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.064522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.543526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.953213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.363417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.770449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:32.229730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:28.879090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.285890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.703407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.106598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.589322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.999154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.410046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.817678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:32.272586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:28.921523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.329818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.747295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.147831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.632114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.042936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.452459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.861129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:32.319023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:28.965630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.375986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.793503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.191228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.677318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.087728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.496355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.907034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:32.362948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.010011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.421708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.838704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.234638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.722789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.132275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.539078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.952756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:32.408444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.053768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.468139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.884094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.278214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.766529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.176572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.583835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.998058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:32.557594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.099927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.516230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:29.930576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.323614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:30.814726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.222119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:31.629022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-28T06:02:32.045254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-28T06:02:33.723905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
voltagecurrentfrequencyenergypower_factoresp32_temperatureworkstation_cpuworkstation_gpuworkstation_ram
voltage1.000-0.9250.132-0.145-0.1450.282-0.690-0.326-0.640
current-0.9251.000-0.0030.0510.012-0.3390.6510.2600.666
frequency0.132-0.0031.000-0.0040.0180.0330.0070.0000.015
energy-0.1450.051-0.0041.0000.4700.2230.4280.1750.354
power_factor-0.1450.0120.0180.4701.0000.2170.4100.2920.215
esp32_temperature0.282-0.3390.0330.2230.2171.0000.0050.134-0.051
workstation_cpu-0.6900.6510.0070.4280.4100.0051.0000.5460.877
workstation_gpu-0.3260.2600.0000.1750.2920.1340.5461.0000.360
workstation_ram-0.6400.6660.0150.3540.215-0.0510.8770.3601.000

Missing values

2023-07-28T06:02:32.611794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-28T06:02:32.708931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

voltagecurrentfrequencyenergypower_factoresp32_temperatureworkstation_cpuworkstation_gpuworkstation_ram
fecha_servidor
2021-05-05 22:05:00119.9966100.97508559.9847460.000000.8767800.00.00.00.0
2021-05-05 22:06:00120.0173910.94695759.9913040.000000.8656520.00.00.00.0
2021-05-05 22:14:00119.9866671.03133360.0000000.020000.8960000.00.00.00.0
2021-05-05 22:15:00120.0350000.95520059.9960000.020000.8693000.00.00.00.0
2021-05-05 22:16:00120.0345450.94254559.9663640.020000.8629090.00.00.00.0
2021-05-05 22:17:00120.0453700.94259359.9796300.020000.8630560.00.00.00.0
2021-05-05 22:18:00120.0378380.94225259.9639640.020000.8626130.00.00.00.0
2021-05-05 22:19:00120.0504670.93869259.9850470.020000.8607480.00.00.00.0
2021-05-05 22:20:00120.0695240.93704859.9761900.028190.8597140.00.00.00.0
2021-05-05 22:21:00120.0460180.93787659.9539820.030000.8603540.00.00.00.0
voltagecurrentfrequencyenergypower_factoresp32_temperatureworkstation_cpuworkstation_gpuworkstation_ram
fecha_servidor
2021-12-04 08:09:00119.2000000.93338059.908264235.4492730.94148850.6749059.5796690.036.792479
2021-12-04 08:10:00119.1941670.94843359.963333233.7247670.94283351.56015710.4716670.036.726083
2021-12-04 08:11:00119.1975000.93522559.976667233.7265330.94141750.6481229.7045830.036.800417
2021-12-04 08:12:00119.1975000.94083359.917500233.7282000.94300050.22682710.2111670.036.838833
2021-12-04 08:13:00119.2000000.93509259.939167233.7299920.94233351.0046029.4390000.036.851000
2021-12-04 08:14:00119.1900000.93435859.965000233.7317500.94158350.8008999.6740000.036.842500
2021-12-04 08:15:00119.2066670.92935859.920000233.7334670.94125050.9629359.6440000.036.877583
2021-12-04 08:16:00119.1840340.92424459.996639231.9798320.94159750.5135139.0831090.036.667815
2021-12-04 08:17:00119.2000000.92519259.950000233.7369330.94133351.3749739.1207500.036.758500
2021-12-04 08:18:00119.1961540.92723159.896154233.7380000.94153853.3333008.7100000.036.764231

Duplicate rows

Most frequently occurring

voltagecurrentfrequencyenergypower_factoresp32_temperatureworkstation_cpuworkstation_gpuworkstation_ram# duplicates
0120.0000000.9460.00000016.600.860.0000000.00.00.02
1120.4964290.1359.976786136.650.3234.4403570.00.00.02
2120.4982140.1359.975000137.030.3234.3616070.00.00.02
3120.4982140.1359.983929136.880.3234.1060710.00.00.02
4120.5000000.1359.969643136.650.3234.5000000.00.00.02
5120.5000000.1359.985714137.120.3233.9980360.00.00.02
6120.5000000.1359.985965137.130.3233.9768420.00.00.02
7120.5000000.1359.987500136.660.3234.4601790.00.00.02
8120.5000000.1359.991071137.020.3234.4205360.00.00.02
9120.5000000.1359.996491136.420.3234.0057890.00.00.02